• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.665-675
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• 2007

The morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. Generally ST segment deviation is concerned with myocardial abnormality. The aim of this study is to detect the change of ST in shape using a polynomial approximation method and the reference ST type. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The detection of QRS complex is accomplished using it's the morphological characteristics such as the steep slope and high amplitude. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes of the specified points between the reference ST set and the least square curve. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.

• Journal of Life Science
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• v.19 no.1
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• pp.81-86
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• 2009

The purpose of the present study was to examine effect of 12 week regular aerobic exercise on ST-segment and QTc interval in middle age type 2 diabetes mellitus (T2DM) patients. The subjects consist of 13 type 2 diabetes mellitus (T2DM) patients in middle age men and all of them had no other complications. Subjects participated in aerobic exercise training for 12 weeks. They started to exercise for $20{\sim}60$ min at $60{\sim}80%$ of $HR_{max}$, (exercise intensity has been increased gradually) per day, $3{\sim}5$ times a week. The results were compared before and after. Weight and BMI, % body fat, fasting glucose, HOMA-IR, $_{peak}DBP$ were significantly decreased and $_{peak}HR$, $_{peak}VO_2$, exercisre time were significantly increased after 12 week aerobic exercise. Also, QTc interval and ST-segment were significantly decreased during at rest, peak exercise after 12 week aerobic exercise. Conclusionally, 12 week aerobic exercise may be improvement in decreased cardiovascular mortality factors (ST-segment) and abnormal autonomic dysfunction (QTc interval) and potentially increased exercise capacity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2223-2230
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• 2011

ECG is used to diagnose heart diseases such as myocardial ischemia, arrhythmia and myocardial infarction. Particularly, myocardial ischemia causes the shape change of the ST segment, this change is transient and may occur without symptoms. So it is important to detect the transient change of ST segment through long term monitoring. ST segment classification algorithm for making diagnosis myocardial ischemia is presented in this paper. The first step in the ST segment shape classification process is to detect R wave point and feature points based adaptive threshold and window. And then, the suggested algorithm detects the ST level change, To classify the ST segment shape, the suggested algorithm uses the slope values of the four points between the S and T wave. The ECG data in the European ST-T database were used to verify the performance of the developed algorithm. The best correct rate was 99.40% and the worst correct rate was 68.48%.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.201-207
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• 2001

This paper describes an automated detection algorithm of ST-T episodes using global curvature which can connect the isoelectric level in ECG and can eliminate not only the slope of ST segment, but also difference of the baseline and global curve. This above method of baseline correction is very faster than the classical baseline correction methods. The optimal values of parameters for baseline correction were found as the value having the highest detection rate of ST episode. The features as input of backpropagation Neural Network were extracted from the whole ST segment. The European ST-T database was used as training and test data. Finally, ST elevation, ST depression and normal ST were classified. The average ST episode sensitivity and predictivity were 85.42%, 80.29%, respectively. This result shows the high speed and reliability in ST episode detection. In conclusion, the proposed method showed the possibility in various applications for the Holter system.